What is Probabilistic Simulation?
In many systems, the controlling parameters, processes and events may be uncertain and/or poorly understood. In a
If the inputs describing a system are uncertain, the prediction of future performance is necessarily uncertain. That is, the result of any analysis based on inputs represented by probability distributions is itself a probability distribution.
Hence, whereas the result of a deterministic simulation of an uncertain system is a qualified statement ("if we build the dam, the salmon population could go extinct"), the result of a probabilistic simulation of such a system is a quantified probability ("if we build the dam, there is a 20% chance that the salmon population will go extinct"). Such a result is typically much more useful to decision-makers who might utilize the simulation results.
GoldSim has powerful capabilities for carrying out probabilistic simulations. In particular, it uses advanced
A detailed description of probabilistic simulation concepts, including a discussion of the advantages and disadvantages of probabilistic simulation and a description of Monte Carlo simulation techniques, is provided in Appendix A of the GoldSim User’s Guide.